Selective provenance for datalog programs using top-k queries

Daniel Deutch, Amir Gilad, Yuval Moskovitch

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

27 Scopus citations

Abstract

Highly expressive declarative languages, such as datalog, are now commonly used to model the operational logic of dataintensive applications. The typical complexity of such datalog programs, and the large volume of data that they process, call for result explanation. Results may be explained through the tracking and presentation of data provenance, and here we focus on a detailed form of provenance (howprovenance), defining it as the set of derivation trees of a given fact. While informative, the size of such full provenance information is typically too large and complex (even when compactly represented) to allow displaying it to the user. To this end, we propose a novel top-k query language for querying datalog provenance, supporting selection criteria based on tree patterns and ranking based on the rules and database facts used in derivation. We propose an effcient novel algorithm based on (1) instrumenting the datalog program so that, upon evaluation, it generates only relevant provenance, and (2) effcient top-k (relevant) provenance generation, combined with bottom-up datalog evaluation. The algorithm computes in polynomial data complexity a compact representation of the top-k trees which may be explicitly constructed in linear time with respect to their size. We further experimentally study the algorithm performance, showing its scalability even for complex datalog programs where full provenance tracking is infeasible.

Original languageEnglish
Title of host publicationProceedings of the VLDB Endowment
PublisherAssociation for Computing Machinery
Pages1394-1405
Number of pages12
Edition12
DOIs
StatePublished - 1 Jan 2015
Externally publishedYes
Event3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006 - Seoul, Korea, Republic of
Duration: 11 Sep 200611 Sep 2006

Publication series

NameProceedings of the VLDB Endowment
Number12
Volume8
ISSN (Electronic)2150-8097

Conference

Conference3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006
Country/TerritoryKorea, Republic of
CitySeoul
Period11/09/0611/09/06

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science (all)

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